Taiwan

IJCNLP-AACL 2022

Random Text Perturbations Work, but not Always

linguistically-informed evaluation

text augmentation

natural language processing

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

workshop paper

The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP).

A registration is required to access this event. Access to the event site will be granted to all registered attendees on **November 16th**. If you have not already done so, please register using the link below:
 
 https://conference.iis.sinica.edu.tw/servlet/Register?ConferenceID=411

Registration Is Required

The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP). 

We present a publicly available corpus with detailed annotations describing the core elements of clinical trials: Participants, Intervention, Control, and Outcomes. The corpus consists of 1011 abstracts of breast cancer randomized controlled trials extracted from the PubMed database. The corpus improves previous corpora by providing detailed annotations for outcomes to identify numeric texts that report the number of participants that experience specific outcomes. The corpus will be helpful for the development of systems for automatic extraction of data from randomized controlled trial literature to support evidence-based medicine. Additionally, we demonstrate the feasibility of the corpus by using two strong baselines for named entity recognition task. Most of the entities achieve F1 scores greater than 0.80 demonstrating the quality of the dataset.

PICO Corpus: A Publicly Available Corpus to Support Automatic Data Extraction from Biomedical Literature

arXiv and Information Extraction

On the portability of extractive Question-Answering systems on scientific papers to real-life application scenarios

Tracking state-of-the-art (SOTA) results in machine learning studies is challenging due to high publication volume. Existing methods for creating leaderboards in scientific documents require significant human supervision or rely on scarcely available LaTeX source files. We propose Table Entity LINker (TELIN), a framework which extracts (task, model, dataset, metric) quadruples from collections of scientific publications in PDF format. TELIN identifies scientific named entities, constructs a knowledge base, and leverages human feedback to iteratively refine automatic extractions. TELIN identifies and prioritizes uncertain and impactful entities for human review to create a cascade effect for leaderboard completion. We show that TELIN is competitive with the SOTA but requires much less human annotation.

TELIN: Table Entity LINker for Extracting Leaderboards from Machine Learning Publications

In this presentation, we introduce a new approach that simulates the construction of phrasal meaning as an analogical process implemented with deep learning techniques. We propose a distributional representation of constructional phrases (namely Relational Embeddings) and a neural network model for generating new embeddings analogically rather than applying traditional compositional operations, which we call ANNE (Analogical Neural Network for Embeddings). 
We experimented with our analogical neural network to understand how it can generalize and be extendable to different scenarios. We argued that the proposed methodology could open the doors to new analyses in distributional semantics as well as in computational models of language processing.

Compositionality as an Analogical Process: Introducing ANNE.


A Comparative Analysis of Stance Detection Approaches and Datasets

Assessing Neural Referential Form Selectors on a Realistic Multilingual Dataset

Assessing Resource-Performance Trade-off of Natural Language Models using Data Envelopment Analysis

This corpus contains more than 30,000 classical Chinese poems in their tokenized form. The poems are from the Complete Tang Poems (全唐詩,CTP) and the Complete Song Poems (全宋詩,CSP). Two groups of annotators from two universities carried out the task. More than 5000 of these poems were annotated repeatedly for the inter-rater agreement analysis. The annotators did not agree with each other perfectly, as many readers may have expected, but the degrees and types of their agreements are worthy of discussion. The annotations support some comments, of the Ming dynasty, about the formats of the poems. We applied machine learning methods to train and test their usability for the tokenization task. We could identify 87% of the words, 77% of the whole lines, and 18% of the whole poems in the CTP correctly. We have expanded the scale of our annotated poems, have extended our exploration with deep learning methods, and we wish to report these extensions in a formal report.

Introducing a Large Corpus of Tokenized Classical Chinese Poems of Tang and Song Dynasties

Research has shown that the practice of translation exhibits predictable linguistic cues that make translated texts distinguishable from original-language texts (a phenomenon known as "translationese"). In this paper, we test the extent to which literary translations are subject to the same effects and whether they also exhibit meaningful differences at the level of content. Research into the function of translations within national literary markets using smaller case studies has suggested that translations play a cultural role that is distinct from that of original-language literature, i.e. their differences reside not only at the level of translationese but at the level of content. Using a dataset consisting of original-language fiction in English and translations into English from 120 languages (N=21,302), we find that one of the principal functions of literary translation is to convey predictable geographic identities to local readers that nevertheless extend well beyond the foreignness of persons and places.

Random Text Perturbations Work, but not Always

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PICO Corpus: A Publicly Available Corpus to Support Automatic Data Extraction from Biomedical Literature

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Random Text Perturbations Work, but not Always

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Next from IJCNLP-AACL 2022

PICO Corpus: A Publicly Available Corpus to Support Automatic Data Extraction from Biomedical Literature

Similar lecture

LADA-Trans-NER: Adaptive Efficient Transformer for Chinese Named Entity Recognition using Lexicon-Attention and Data-Augmentation

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads